Plotting Data

We start by installing and loading the required packages. ggplot2 is included in the tidyverse package so you can just install the tidyverse package and that will automatically install ggplot2

install.packages("tidyverse")
install.packages("gridExtra")
install.packages("RColorBrewer")
install.packages("colorspace")

library(tidyverse)

There are three different plotting systems in R: base, lattice, and ggplot2. We will primarily focus on ggplot2 with a couple of brief diversions into base graphics.

RStudio ggplot2 cheat sheet

Overview of `ggplot2`

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatterplot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 requires tidy data: i.e., a column for every dimension, and a row for every observation. Well structured data will save you lots of time when making figures with ggplot2.

ggplot2 graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a plot, we need to:

use the ggplot() function and bind the plot to a specific data frame using the data argument

ggplot(data = helpdata)

define aesthetics (aes), by selecting the variables to be plotted and the variables to define the presentation such as plotting size, shape color, etc.

ggplot(data = helpdata, aes(x = cesd, y = mcs))

add geoms – graphical representation of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones, including:
- geom_point() for scatter plots, dot plots, etc.
- geom_boxplot() for, well, boxplots!
- geom_line() for trend lines, time-series, etc.

To add a geom to the plot use + operator.

Longitudinal data can be visualized as a line graph with time on the x-axis. For these plots, we need the longhelp data that we created.

ggplot(data = longhelp, aes(x = time, y = cesdtv)) +
     geom_line()

## Warning: Removed 68 rows containing missing values (geom_path).

Unfortunately, this does not work because we plotted data for all individuals together. We need to tell ggplot() to draw a line for each individual by modifying the aesthetic function to include group = id:

ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id)) +
    geom_line()

## Warning: Removed 740 rows containing missing values (geom_path).

There are a lot of individuals so one thing we could do is to randomly sample 20 individuals. Note that you need the unique() function, which gives you the unique values of a variable. We use this so that we do not sample the same id twice.

# randomly sampling 20 individuals
ids <- sample(unique(longhelp$id), 20)
# creating dataframe of the 20 individuals
samp <- longhelp[longhelp$id %in% ids, ]
ggplot(data = samp, aes(x = time, y = cesdtv, group = id)) +
    geom_line()

## Warning: Removed 40 rows containing missing values (geom_path).

We will be able to distinguish individuals in the plot if we add colors:

ggplot(data = samp, aes(x = time, y = cesdtv, group = id, color = id)) +
    geom_line()

## Warning: Removed 40 rows containing missing values (geom_path).

What happens to the legend and color scheme if we treat id as a factor?

ggplot(data = samp, aes(x = time, y = cesdtv, group = id, color = as.factor(id))) +
    geom_line()

## Warning: Removed 40 rows containing missing values (geom_path).

Another option when there are so many individuals is try faceting if you do not want to select a random sample.

Faceting

ggplot has a special technique called faceting that allows the user to split one plot into multiple plots based on a factor included in the dataset. We will use it to make a plot for each substance:

ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id)) +
    geom_line() +
    facet_wrap(~ substance)

## Warning: Removed 740 rows containing missing values (geom_path).

We can now make the faceted plot by splitting further by sex using color:

ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id, color = female)) +
     geom_line() +
     facet_wrap(~ substance)

## Warning: Removed 740 rows containing missing values (geom_path).

Usually plots with white background look more readable when printed. We can set the background to white using the function theme_bw(). Additionally, you can remove the grid:

 ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id, color = female)) +
     geom_line() +
     facet_wrap(~ substance) +
     theme_bw() +
     theme(panel.grid = element_blank())

## Warning: Removed 740 rows containing missing values (geom_path).

`ggplot2` themes

In addition to theme_bw(), which changes the plot background to white, ggplot2 comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at http://docs.ggplot2.org/current/ggtheme.html. theme_minimal() and theme_light() are popular, and theme_void() can be useful as a starting point to create a new hand-crafted theme.

The ggthemes package provides a wide variety of options (including an STATA theme). The ggplot2 extensions website provides a list of packages that extend the capabilities of ggplot2, including additional themes.

The facet_wrap geometry extracts plots into an arbitrary number of dimensions to allow them to cleanly fit on one page. facet_wrap() wraps around like words on a page whereas facet_grid() does not. The facet_grid geometry allows you to explicitly specify how you want your plots to be arranged via formula notation (rows ~ columns; a . can be used as a placeholder that indicates only one row or column).

Let’s modify the previous plot to compare how the CES-D of males and females has changed through time:

# One column, facet by rows
ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id, color = id)) +
    geom_line() +
    facet_grid(female ~ .)

## Warning: Removed 740 rows containing missing values (geom_path).

# One row, facet by column
ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id, color = id)) +
    geom_line() +
    facet_grid(. ~ female)

## Warning: Removed 740 rows containing missing values (geom_path).

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Customization

Now, let’s change names of axes to something more informative than ‘time’ and ‘cesdtv’ and add a title to the figure:

ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id, color = female)) +
    geom_line() +
    facet_wrap(~ substance) +
    labs(title = "CESD by Gender and Substance across Months",
         x = "Months",
         y = "CESD") +
    theme_bw()

## Warning: Removed 740 rows containing missing values (geom_path).

The axes have more informative names, but their readability can be improved by increasing the font size:

ggplot(data = longhelp, aes(x = time, y = cesdtv, group = id, color = female)) +
    geom_line() +
    facet_wrap(~ substance) +
    labs(title = "CESD by Gender and Substance across Months",
        x = "Months",
        y = "CESD") +
    theme_bw() +
    theme(text=element_text(size = 14))

## Warning: Removed 740 rows containing missing values (geom_path).

Note that it is also possible to change the fonts of your plots. If you are on Windows, you may have to install the extrafont package, and follow the instructions included in the README for this package.

Colors

Try using different color palettes (see http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/).

There are two packages that contain various color palettes. The first is RColorBrewer.

library(RColorBrewer)
display.brewer.all()

The other package is colorspace. An example of some of the color palettes available in the colorspace package

Be careful with colors because some people are colorblind. In fact, there are colorblind palettes. Here are two; the first with grey and the second with black.

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

And this is what they look like:
cbPalette

cbbPalette

Arranging and exporting plots

Faceting is a great tool for splitting one plot into multiple plots, but sometimes you may want to produce a single figure that contains multiple plots using different variables or even different data frames. The gridExtra package allows us to combine separate ggplots into a single figure using grid.arrange():

library(gridExtra)

boxplot <- ggplot(data = helpdata, aes(x = racegrp, y = cesd)) +
  geom_boxplot() +
  xlab("Race") + ylab("CESD")

plot <- ggplot(data = longhelp, aes(x=time, y=cesdtv, group=id, color=female)) +
  geom_line() + 
  xlab("Months") + ylab("CESD")

grid.arrange(boxplot, plot, ncol = 2, widths = c(4, 6))

## Warning: Removed 740 rows containing missing values (geom_path).

In addition to the ncol and nrow arguments, used to make simple arrangements, there are tools for constucting more complex layouts.

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi):

my_plot <- ggplot(data =longhelp, aes(x=time, y=cesdtv, group=id, color=female)) +
    geom_line() +
    facet_wrap(~ substance) +
    labs(title = "CESD by Gender and Substance across Months",
        x = "Month",
        y = "CESD") +
    theme_bw()

ggsave("my_plot.png", my_plot, width = 15, height = 10)

## This also works for grid.arrange() plots
combo_plot <- grid.arrange(boxplot, plot, ncol = 2, widths = c(4, 6))
ggsave("combo_plot.png", combo_plot, width = 10, dpi = 300)

Note: The parameters width and height also determine the font size in the saved plot.

Week 2: Introduction to Visualizing Multilevel Data and Matrices

Donna L. Coffman

1/23/2020

Data Manipulation

Wide to Long Data Format

Long to Wide Data Format

Plotting Data

Overview of `ggplot2`

Faceting

`ggplot2` themes

Customization

Colors

Arranging and exporting plots

Week 2: Introduction to Visualizing Multilevel Data and Matrices

Donna L. Coffman

1/23/2020

Data Manipulation

Wide to Long Data Format

Long to Wide Data Format

Plotting Data

Overview of ggplot2

Faceting

ggplot2 themes

Customization

Colors

Arranging and exporting plots

Overview of `ggplot2`

`ggplot2` themes